Control charts that are typically based on the assumption of a specific form of a parametric distribution, such as the normal, are called parametric control charts. however, there is not enough information to justify this assumption the distribution assumption of the data is not met or there is not enough evidence showing that the assumption is met. It is well known that the performance of many parametric control charts can be seriously degraded in situations like this. Thus, control charts that do not require a specific distributional assumption to be valid, the so-called nonparametric or distribution-free charts, are desirable in practice. Nonparametric charts have increasingly become viable alternatives to parametric counterparts in detecting process shifts when the underlying process output distribution is unknown, specifically when the process measurement is multivariate. In this paper, two simple to use multivariate nonparametric control charts are considered. The charts are Shewhart-type charts and are based on the multivariate forms of the sign and the Wilcoxon signed-rank tests. The charts were compared with those of multivariable control parametric chart exists.